Research on Consumer Choice Behavior by Reviews on Expedia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Expedia is a travel searching and booking platform.The dataset specifically showcases property searches on the platform.To help Expedia better know how to attract more consumers, by studying the association between reviews and consumers' choices by using hypothesis test and multiple linear regressions.The results of the study found that reviews can significantly affect consumers' choice behavior.Secondly, consumers tend to view properties with Review counts less than 5000 and with higher Average Guest Rating.Thirdly, consumers prefer to give higher individual ratings to properties with more Review Count.Lastly, consumers tend to choose properties with Star Rating at 4, and properties with higher Star Ratings tend to have obviously more Review Counts.The research results of this paper can make some policy suggestions for managers engaged in this field and have important practical significance in order to regulate the healthy development of the platform.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it